Intelligent Agent

ABSTRACT

A computing system aggregates information from a plurality of information channels associated with a computing device and a user of the computing device. A user configures the access for the computing system to specific information channels at a user interface. Based on a knowledge base and by machine learning techniques, the computing system analyzes the aggregated information to identify information relevant to an intelligent action for execution on behalf of the user. The computing system identifies the intelligent action in the context of the user&#39;s preferences and permissions granted to the computing system. The computing system initiates execution of the intelligent action based on a confidence level derived from analysis of information contained the knowledge base and historical decisioning information. The computing system receives feedback for an executed action and incorporates the feedback in the knowledge base for future decisioning based on aggregated information.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to patentapplication Ser. No. 16/913,530 entitled “Intelligent Agent” filed May26, 2020, which is incorporated by reference in its entirety.

BACKGROUND

Aspects of the disclosure relate to a system for aggregating informationassociated with an end user from a number of configured channels andproviding personalized responses. One or more aspects of the disclosurerelate to an intelligent agent system capable of aggregating end userinformation and initiating, via machine learning, autonomousdecision-based responses based on learned end user behavior andpreferences.

Today, a number of computing systems associated with a variety ofcomputing devices (e.g., mobile devices, mobile phones, and the like)act as virtual assistants, responding to end user input and providinggeneric responses to end user input. These systems typically generategeneric responses such as, for example, setting a calendar appointment,providing traffic data on route to a destination, and transmittingmessages based on received input from an end user. However, thesesystems lack personalized, autonomous decision-based responses togathered information that execute on behalf of the end user, as well aslacking the ability to gather information from a number of configurableinformation input channels associated with the end user.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the disclosure. The summary is not anextensive overview of the disclosure. It is neither intended to identifykey or critical elements of the disclosure nor to delineate the scope ofthe disclosure. The following summary merely presents some concepts ofthe disclosure in a simplified form as a prelude to the descriptionbelow.

Aspects of the disclosure provide effective, efficient, scalable, andconvenient technical solutions that address and overcome the technicalproblems associated with aggregating end user information and executingpersonalized actions associated with a number of systems on behalf ofthe end user.

In some cases, a computing device (e.g., a mobile cellular device) mayinclude an intelligent agent system, such as an application for thecomputing device. The intelligent agent system may include a userinterface accessible via a display device coupled to the computingdevice. Using the user interface, a user may configure access rights(e.g., grant, revoke, deny, and the like) to the intelligent agentsystem to allow access to one or more information channels (e.g., socialmedia platforms, internet browsing data, financial information, locationdata, user calendar information, and the like) associated with the user.The information channels may include data sourced from hardwarecomponents associated with the computing device (e.g., a locationtracking device, an ambient light sensor, an accelerometer, userinterface devices, pressure sensors, and the like), linked user accountinformation (e.g., social media accounts, bank accounts, email accounts,instant messaging accounts, and the like), and systems and/orapplications accessible by the computing device (e.g., mobileapplications, calendar applications, calendar information, callinformation, contact lists, and the like). In some cases, theintelligent agent system may allow a user to input informationaccessible by the intelligent agent system (e.g., a user may input apersonal savings goal via a user interface screen displayed by the userinterface). The intelligent agent system may include an aggregationmodule for centrally aggregating information gathered from the one ormore configured information channels. For example, the aggregationmodule may receive and/or store information aggregated from theinformation channels using explicit methods (e.g., user providedinformation for preferences, interests, settings, and the like) and/orimplicit methods (e.g., analysis of passive user data from informationchannels).

In some cases, the intelligent agent system may include a machinelearning module to enable intelligent information processingcapabilities. For example, the machine learning module may sort and/oranalyze the information received and/or fetched by the aggregationmodule to extract relevant information for autonomous decision-makingprocesses performed by the intelligent agent system. In some cases, theintelligent agent system may include data stores associated with aknowledge base. The knowledge base may include information associatedwith the granted autonomous decision-making capabilities of theintelligent agent system (e.g., the automatic actions that theintelligent agent system is configured to execute). In some cases, theknowledge base may include information that identifies the systemsand/or applications of the computing device that the intelligent agentsystem may access and/or modify to perform an associated action. Forexample, the knowledge base may contain permission informationcorresponding to automated actions to be initiated via one or moremobile device applications.

In some cases, the intelligent agent system may include one or more datastores associated with performance data (e.g., historical decisioninginformation for previously executed decisions, decision feedbackinformation, and the like) of the intelligent agent system. The one ormore data stores may include decision data (e.g., the decision made, theconfidence level in the decision, feedback received from the user forthe decision, alternative decisions that may have been made) for eachexecuted decision of the intelligent agent system. In some cases, theintelligent agent system may include a decision processing module. Thedecision processing module may access and/or receive information fromthe machine learning module, the data stores associated with theknowledge base, and the data stores associated with performance data ofthe intelligent agent system. The decision processing module may analyzethe received information, determine a confidence level for a decision,and/or initiate execution of the decision (e.g., trigger an action to beexecuted by one or more mobile device applications and/or systems) basedon the determined confidence level compared to a confidence threshold.In some cases, the decision processing module may analyze input receivedat the user interface associated with decision-making processes. Thedecision processing module may access, modify, and/or otherwise directthe configured applications and/or systems of the computing device toexecute one or more processes associated with decisions. After adecision is made and a corresponding action is executed based ondirection from the decision processing module, the decision processingmodule may store log information associated with the decision in the oneor more data stores associated with the knowledge base and/orperformance information. In some cases, for a decision involving anotification to be displayed at the computing device, the decisionprocessing module may generate and/or trigger an application and/orsystem of the computing device to generate a notification for display ata display device of the computing device, where the notification may bedisplayed at the user interface module of the intelligent agent system,a notification system installed as part of an operating system of thecomputing device, and/or another method to display the notification.

These features, along with many others, are discussed in greater detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIG. 1 depicts an illustrative computing environment for the intelligentagent system in accordance with one or more aspects described herein;

FIG. 2A shows an illustrative event sequence for decision processing ofaggregated information in the intelligent agent system in accordancewith one or more aspects described herein;

FIG. 2B shows an illustrative event sequence for decision processing ofaggregated information in the intelligent agent system in accordancewith one or more aspects described herein;

FIG. 3 shows an illustrative operating environment in which variousaspects of the disclosure may be implemented in accordance with one ormore aspects described herein; and

FIG. 4 shows an illustrative block diagram of workstations and serversthat may be used to implement the processes and functions of certainaspects of the present disclosure in accordance with one or more aspectsdescribed herein.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. It isto be understood that other embodiments may be utilized, and structuraland functional modifications may be made, without departing from thescope of the present disclosure.

It is noted that various connections between elements are discussed inthe following description. It is noted that these connections aregeneral and, unless specified otherwise, may be direct or indirect,wired or wireless, and that the specification is not intended to belimiting in this respect.

The above-described examples and arrangements are merely some examplearrangements in which the systems described herein may be used. Variousother arrangements employing aspects described herein may be usedwithout departing from the invention.

Today, a number of computing devices are equipped with systems and/orapplications intended to execute the functionality of a virtualassistant, personal administrator, and the like. Such systems and/orapplications typically respond, based on a number of generic functions,to received user input and lack automated, autonomous decision-makingcapabilities. More systems and/or applications have been limited tosingle areas of use, including areas such as specialized assistantapplications for financial assistance, travel assistance, and timemanagement functionality (e.g., a calendar tool). Additionally, theseexisting systems and/or applications lack comprehensive userconfiguration settings to configure automated access to specified userinformation by the systems and/or applications. As such, a need has beenidentified for an intelligent agent system capable of aggregatinginformation from a number of information channels associated with a userand/or a computing device (e.g., a mobile cellular device) to performautonomous decision-making processes on behalf of the user.

In some cases, a computing device, such as a mobile cellular device, mayinclude an intelligent agent system. The intelligent agent system may beinstalled as an application by storing executable code in one or morememory devices communicatively coupled to the computing device. Theintelligent agent system may be configured to access one or moreapplications and/or systems installed on or associated with thecomputing device and may autonomously initiate one or more actionsassociated with the applications and/or systems. In some cases, theintelligent agent system may include a user interface module that may beaccessible via a display device coupled to the computing device. A usermay input one or more commands at the user interface module and/or auser may input information associated with the user (e.g., user accountinformation, user preferences, and the like) at the user interfacemodule. For example, a user may enter bank account information at theuser interface to grant permission for the intelligent agent system toanalyze and learn from a user's financial information.

In some cases, at the user interface module, a user may grant theintelligent agent system access to one or more applications, systems,user accounts, and/or other user information associated with thecomputing device, such as through directly inputting login credentialsat the user interface module and granting the intelligent agent systemaccess to an application of the computing device. Additionally, the userinterface module may allow the user to grant the intelligent agentsystem access to the hardware components of the computing device, suchas a location tracking device (e.g., a global positioning system (GPS)device), an ambient light sensor, an accelerometer, a touch-sensitivedisplay device, and the like. The user interface module may allow a userto grant and/or revoke access to one or more information channels (e.g.,applications, systems, computing device usage information, and the like)associated with the user and/or computing device that interface with theintelligent agent system. In some cases, one or more notificationsassociated with the decisioning processes of the intelligent agentsystem may be centrally location at the user interface module for accessand/or further action.

In some cases, the intelligent agent system may include an aggregationmodule. The aggregation module may centrally monitor, aggregate, and/oraccess information at the one or more configured information channelsfor the intelligent agent system. The aggregation module maycontinuously and/or periodically fetch information from the one or moreinformation channels. The aggregation module may access the informationchannels that may include data sourced from hardware componentsassociated with the computing device (e.g., a location tracking device,an ambient light sensor, an accelerometer, a touch-sensitive displaydevice, and the like), user account information (e.g., social mediaaccounts, bank accounts, email accounts, instant messaging accounts, andthe like), and/or systems of the computing device (e.g., an internetbrowser, applications, contact lists, calendar information, computerdevice usage information, and the like). In some cases, the aggregationmodule may access information at a specific information channel based ona user accessing the specific information channel (e.g., the aggregationmodule may fetch the information associated with the application duringthe time of user access). Additionally or alternatively, the aggregationmodule may access and/or fetch information from an information channelas it is received and/or otherwise made available at the computingdevice (e.g., interrupt based access). For example, if a text message isreceived at the computing device via a network, the aggregation modulemay immediately access and analyze the information contained in the textmessage, rather than waiting for a preconfigured interval and/or time.

In some cases, the intelligent agent system may include a machinelearning module. The machine learning module may operate based onsemi-supervised support vector machine learning techniques. The machinelearning module may enable the intelligent agent system to analyzeprevious decision-making processes in combination with newly receivedinformation (e.g., information channel information, user feedbackinformation) to formulate and/or revise decision-making processes andmodels. The machine learning module may continuously and/or periodicallyanalyze information analyze the aggregated information from theaggregation module to determine the relevance of the aggregatedinformation to decision-making processes.

In some cases, the intelligent agent system will include one or moredata stores associated with a knowledge base for the intelligent agentsystem. The knowledge base may include information associated with userconfiguration preferences and/or settings for the intelligent agentsystem (e.g., information indicating that the intelligent agent systemmay and/or not access information associated with an instant messagingapplication of the computing device). In some cases, the information ofthe knowledge base will be used to generate a user profile for the userassociated with the computing device. The user profile may beautomatically updated, altered, and/or otherwise changed based oninformation received from the information channels to maintain anaccurate assessment of the user's preferences related to the intelligentagent system. The machine learning module may analyze the relevantinformation sourced from the aggregation module in context of theconfiguration settings and/or preferences of the knowledge base. In somecases, the knowledge base may include comprehensive informationindicating the systems, applications, and or settings associated withthe computing device that the intelligent agent system may potentiallyaccess (e.g., the systems for which the intelligent agent may performautomatic decision-making processes on behalf of the user). If themachine learning module analyzes the relevant information and determinesthe information may result in and/or contribute to a new decisioningprocess, the machine learning module may add the relevant information toknowledge base.

In some cases, the intelligent agent system may include one or more datastores associated with decisioning performance information (e.g.,historical information associated with previous decisioning processes ofthe intelligent agent system). The one or more data stores may includeinformation associated with previous decisioning processes including thedecision made, the result of the decision (e.g., the action initiatedfor execution by the intelligent agent system), user feedback associatedwith the decision, the confidence level in the decision, and the like.Such information may be made available for a decisioning processingmodule to initiate one or more executable actions.

In some cases, the intelligent agent system may include a decisionprocessing module. The decision processing module may access and/orreceive information from the machine learning module and the one or moredata stores associated with the knowledge base and/or decisioningperformance information. The decision processing module may analyze thereceived information from the machine learning module, knowledge basedata stores, and/or performance information data stores to formulate adecision and/or action associated with a system, application, and/orfunction of the computing device. In some cases, the decision processingmodule may determine a confidence level for a decision and compare thedetermined confidence level to configured threshold. If the userconfiguration information of the knowledge base permits the decisionprocessing module to initiate execution of the decision, the decisionprocessing module may initiate execution of the decision at theassociated application and/or system. In some cases, the decisionprocessing module may process input received at the user interfaceassociated with user permission for a specific decision-making process.After a decision is made and a corresponding action is initiated by thedecision processing module, the decision processing module may store loginformation related to the decision in the one or more data storesassociated with the knowledge base and/or performance data.

FIG. 1 shows an illustrative computing environment 100 for anintelligent agent system 120 in accordance with one or more aspectsdescribed herein. The illustrative computing environment 100 may includeone or more networks 115 (e.g., a telecommunications network, theInternet, a Wi-Fi network, and the like). In some cases, the network 115may be a wired or wireless network, such as the networks described belowwith respect to FIGS. 3 and 4. The illustrative computing environment100 may include one or more computing devices 101 (e.g., computingdevice 102, computing device 104, computing device 106). The one or morecomputing devices 101 may include one or more characteristics withrespect to those of the intelligent agent computing device 301 of FIG.3. The one or more computing devices 101 may be associated with one ormore users. In some cases, a user may be associated with more than onecomputing device. The one or more computing devices 101 may be one of amobile device, a tablet, a laptop computer, a desktop computer and/orthe like. In some cases, the one or more computing devices 101 may be amobile cellular device. The one or more computing devices 101 may becommunicatively coupled to the one or more networks 115 of theillustrative computing environment 100. The one or more computingdevices 101 may include one or more memory 110. The memory 110 mayinclude one or more computer-readable media devices as described hereinfor FIG. 3. Software (e.g., computer-readable instructions) may bestored within the memory 110 and/or another digital storage of the oneor more computing devices 101 to provide instructions to a processor forenabling the one or more computing devices 101 to perform variousfunctions as discussed herein. In some cases, the one or more computingdevices 101 may include a display device. The one or more computingdevices 101 may receive one or more inputs via one or more input devices(e.g. a touch-sensitive display, a keyboard, a microphone, a camera, andthe like). The one or more computing devices 101 (e.g., computing device104, computing device 106) may include the same contents,characteristics, and/or functionality as described herein for computingdevice 102.

In some cases, the memory 110 of the computing devices 102 may storeexecutable code corresponding to one or more applications 160. The oneor more applications 160 may include, email client platformapplications, messaging applications, social media platformapplications, internet browser applications, accessibility applications,consumer account-based applications (e.g., financial applications,retail applications), and the like. The one or more applications 160 maycorrespond to one or more accounts associated with the user of thecomputing device 102. For example, the application 162 (e.g., a bankingapplication may include a login user interface screen for a user toinput account information (e.g., a username and password). The user ofthe computing device 102 may add and/or remove applications to thecomputing device 102. Further, one or more of the applications 160 mayreceive and/or send information via the network 115 (e.g., atelecommunications network).

In some cases, the memory 110 of the computing device 102 may storeexecutable code corresponding to one or more systems 170. The one ormore systems 170 may be systems associated with the native functionalityof the computing device 102 (e.g., an operating system and theassociated sub-systems). For example, the operating system of mobilecellular device (e.g., computing device 102) may include a system 172 toinitiate and receive phone calls. Associated systems (e.g., systems 170)for the phone call system may include a voicemail system, a contact listsystem, and the like. In some cases, systems of the computing device 102may collect data from one or more hardware devices (e.g., sensors,input/output devices, and the like) of the computing device 102.Hardware devices of the computing device 102 may include ambient lightsensors, location tracking devices (e.g., a GPS device), accelerometers,gyroscopes, infrared sensors, microphones, speakers, and the like.

In some cases, the memory 110 of the computing device 102 may includeone or more system information data stores 180. The one or more systeminformation data stores 180 may include information gathered from one ormore systems 170, hardware devices of the computing device 102, andnetworks (e.g., network 115) to which the computing device 102 isconnected. For example, a system information data store 182 may includeusage information for the computing device 102 (e.g., instances thecomputing device 102 has been physically held by a user, computingdevice 102 movement information, duration of time the computing devicehas been operated, and the like), display device information (e.g.,brightness, user input speed at the display device), computing device102 location information (e.g., location data gathered from a GPSdevice), and information received via the network 115 associated with asecond user of the intelligent agent system 120 for the computing device104. In some cases, the system information data stores 180 may includeaccount information (e.g., username, password, login credentials)associated with a user of the intelligent agent system 120, where theaccount information may be input by a user at a user interface module124. The account information stored system information data stores 180may allow the intelligent agent system 120 to access the informationassociated with the account information for decisioning processes asdescribed herein.

In some cases, the memory 110 of the computing device 102 may storeexecutable code for operation of an intelligent agent system 120. Theintelligent agent system 120 may be installed as an application 162 inthe memory 110 of the computing device 102, as a system 172 installed onthe computing device, and/or integrated into one or more differentcomponents of the computing device 102. The intelligent agent system 120may be granted access to one or more of the applications 160, systems170, and/or system information data stores 180 of the computing device102. The intelligent agent system 120 may be granted access to the oneor more hardware devices associated with the computing device 102. Insome cases, the intelligent agent system 120 may initiate execution ofone or more processes and/or actions at the one or more applications 160and/or systems 170 of the computing device 102. The intelligent agentsystem 120 may initiate execution of one or more processes and/oractions autonomously and/or based on received input from a user of thecomputing device 102. For example, the intelligent agent system 120 mayprocess an algorithm to automatically analyze the user's activities withone or more applications 160 and/or systems 170 of the computing device102, where the intelligent agent system 120 may send an output totrigger an action (e.g., trigger execution of a function to an increasea user's credit card limit in a financial application based on adeclined transaction associated with the user's credit card) within anapplication 162 (e.g., a financial application installed on thecomputing device 102) and/or at a remote computing system (e.g., abanking computing system associated with the financial application).Additionally, for example, the intelligent agent system 120 mayautomatically analyze a user's social media activity and internetbrowser history and generate a notification for display at a userinterface of the computing device (e.g., computing device 102) thatcontains information recommending the purchase of one or more consumerproducts. As another example, a user may input accessibility preferencesassociated with a condition (e.g., blindness) and the intelligent agentsystem 120 may initiate a read-aloud feature for voice text messagesassociated with a messaging application (e.g., of the one or moreapplications 160) out of a speaker coupled to the computing device 102.In some cases, the intelligent agent system may analyze user inputs tothe computing device 102 and/or outputs from the computing deviceconcurrently with the actions, periodically, or in response to an action(e.g., an interrupt notification).

In some cases, the intelligent agent system 120 may include a userinterface module 124. The user interface module 124 may be accessible ata display device coupled to the computing device 102. The intelligentagent system 120 may receive one or more inputs from the user at theuser interface module 124 via one or more input devices as describedherein for the computing device 102. In some cases, at the userinterface module 124, the user may configure the access settings for theintelligent agent system 120. The user may grant or deny the intelligentagent system 120 access to information associated with specificapplications 160, systems 170, and/or system information data stores 180of the computing device 102. For example, the user may configure theintelligent agent system 120 to analyze and/or access the informationassociated with an email client application (e.g., application 162),location tracking device (e.g., a GPS device) information, social mediaaccount information, and messaging information associated with amessaging application (e.g., application 162). In some cases, aconfiguration user interface screen provided by the user interfacemodule 124 may include an area for a user to enter one or more logincredentials (e.g., usernames, passwords, and the like) for accountinformation associated with the user, where the information accessiblevia the account login credentials may be made available for access bythe intelligent agent system 120. The one or more login credentials foraccount information may be stored in the one or more system informationdata stores 180.

In some cases, at the user interface module 124, the user may access aconfiguration user interface screen to grant or deny the intelligentagent system 120 the ability to autonomously initiate execution ofprocesses and/or actions associated with specific applications 160,systems 170, system information data stores 180, and or other elementsof the computing device 102. For example, the user may enter apermission configuration on a configuration user interface screen togrant permission for the intelligent agent system 120 to automaticallyset a travel notice for the geographic location in a financialapplication (e.g., application 162) associated with a user's creditcard, based on aggregated information that indicates a user is travelingto a certain geographic location. In some cases, the intelligent agentsystem 120 may include a decisioning system 130 associated with theautonomous decisioning processes of the intelligent agent system 120.The user interface module 124 may access and modify elements of thedecisioning system 130 based on the received input at the user interfacemodule 124. In some cases, elements of the decisioning system 130 maygenerate one or more notifications for display at the user interfacemodule 124 and/or request feedback from a user at the user interfacemodule 124 for specific decision-making processes.

In some cases, the decisioning system 130 may including an aggregationmodule 132. The aggregation module 132 may receive and/or otherwiseaggregate information from the one or more information channels 114(e.g., applications 160, systems 170, system information data stores180) of the computing device 102. In some cases, the intelligent agentsystem 120 may be included with an existing application 162 and begranted access to the information contained therein. For example, theintelligent agent system 120 may be included functionality of anexisting financial application (e.g., application 162) and be grantedaccess to user account information (e.g., checking account, savingsaccount, credit card account) associated with the financial application.

In some cases, the aggregation module 132 may monitor specificinformation channels 114 to receive and/or aggregate informationassociated with decision-making processes of the intelligent agentsystem 120. In some cases, the aggregation module 132 will receiveand/or aggregate information from an information channel as it isreceived and/or made available at the computing device 102. For example,an email client application (e.g., application 162) accessible to theintelligent agent system 120 may receive an email message via thenetwork 115 by synchronizing with a remote email client server. Theaggregation module 132 may receive the contents of the email message inthe intelligent agent system 120 as the email client application (e.g.,application 162) receives the email message. Additionally oralternatively, for example, the aggregation module 132 may receive thecontents of the email message in the intelligent agent system 120 whenthe aggregation module 132 periodically fetches information (e.g., everyminute, every 10 minutes) from the email client application (e.g.,application 162).

In some cases, the aggregation module 132 may fetch informationcontinuously and/or periodically from an information channel. Forexample, the aggregation module 132 may fetch location informationassociated with the computing device from a data store (e.g., a systeminformation data store 182) of the computing device 102. If the locationdata is dated beyond a certain period of time (e.g., an hour), theaggregation module 132 may fetch location information from a system(e.g., system 172) associated with a location tracking device (e.g., aGPS device) of the computing device 102 and/or from an application(e.g., application 162) that tracks the location information of thecomputing device 102 (e.g., a maps application). In some cases, theaggregation module 132 may fetch information based on received inputfrom a user at the user interface module 124. For example, a user mayenter a savings goal at the user interface module 124 and in response,the aggregation module 132 may fetch financial information associatedwith the user from a financial application (e.g., 162).

In some cases, the aggregation module 132 may receive informationassociated with one or more users of one or more alternate computingdevices (e.g., computing device 104, computing device 106) via thenetwork 115, where the computing devices are equipped with anintelligent agent system 120. A user of a computing device (e.g.,computing device 102) may configure the ability to share informationaggregated at the aggregation module 132 with other users of theintelligent agent system 120. For example, a first user of the computingdevice 102 may grant access to specific user information (e.g., locationdata) for a second user of the computing device 104 at the userinterface module 124, where the first user and second user are mutualcontacts in a contact list system (e.g., system 172) associated withboth the computing device 102 and the computing device 104. In somecases, the aggregation module 132 may receive information associatedwith the user for one or more alternate computing devices (e.g.,computing device 104, computing device 106) that are also operated bythe user via the network 115, where the computing devices are equippedwith an intelligent agent system 120. A user of two or more computingdevices (e.g., computing device 102, computing device 104, computingdevice 106) may configure the ability to share information aggregated atthe aggregation module 132 of each computing device associated with theuser among each of the commonly operated computing devices associatedwith the user. For example, a user may operate a personal mobilecellular device (e.g., computing device 102) and a business mobilecellular device (e.g., computing device 104) and configure the abilityto exchange aggregated information from the aggregation module 132 ofthe personal mobile cellular device and the business mobile cellulardevice, allowing for aggregation of information from multiple computingdevices (e.g., computing device 102, computing device 104) associatedwith the user.

In some cases, the intelligent agent system 120 may include a machinelearning module 134. The machine learning module 134 may receive theaggregated information of the one or more information channels 114 fromthe aggregation module 132. The machine learning module 134 maycontinuously and/or periodically receive information from theaggregation module 132. In some cases, the machine learning module 134may analyze the received information using semi-supervised supportvector machine learning techniques. Additionally or alternatively, insome cases, the machine learning module 134 may utilize superviseddecisioning algorithms (e.g., regression, decision tree, neuralnetworks, and the like), unsupervised decisioning algorithms (e.g.,Apriori algorithms, K-means, and the like), and/or reinforcementdecisioning techniques (e.g., Markov decision processes). In some cases,the machine learning module 144 may analyze, based on a superviseddecisioning algorithm, an unsupervised decisioning algorithm, areinforcement decisioning algorithm, and the like, the receivedinformation from the aggregation module. The machine learning module 134may analyze the received information to determine if the receivedinformation from the aggregation module 132 may result in executableaction as a result of the decision-making process of the decisioningsystem 130. In some cases, the machine learning module 134 may betrained by one or more sets of training data in order to identify if thereceived information may result in executable action. The results of theanalyzed training data and/or received feedback from the user may bestored in one or more knowledge base data stores 136 and used tocontinuously train the machine learning module 134.

In some cases, the decisioning system 130 may include one or moreknowledge base data stores 136. A knowledge base data store 136 maycontain information associated with one or more user configurations,preferences, and/or settings for the intelligent agent system 120. Forexample, the knowledge base data store(s) 136 may include informationreceived at the user interface module 124 that denies the intelligentagent system 120 permission to access a social media application (e.g.,application 162) of the computing device 102. Additionally, for example,the knowledge base data store(s) 136 may include information grantingthe intelligent agent system 120 the ability to access a messagingsystem (e.g., system 172) information, however, also denying theintelligent agent system 120 the ability to automatically send a replymessage in response to a received message. In some cases, the knowledgebase data store(s) 136 may include generic training data informationassociated with decision-making processes of the intelligent agentsystem 120 and/or results of training data provided to the machinelearning module 134. The generic training data information and/orresults of training data provided to the machine learning module 134 mayenable the intelligent agent system 120 the ability to initiateexecution of actions on behalf of the user, prior to developing acomprehensive user profile in the knowledge base data store(s) 136. Forexample, the knowledge base data store(s) 136 may be prefilled withinformation indicating that if a user books airplane tickets to travelto a location, the user may require corresponding lodging and rental caraccommodations at the destination location. Additionally, for example, aknowledge base data store 136 may include information indicating that auser has granted permission to the intelligent agent system 120 toautomatically calculate income tax information, where the calculation isbased on information derived from a financial application (e.g., 162) ofthe computing device 102.

In some cases, the combination of the information of the one or moreknowledge base data stores 136 with information received at theaggregation module 132 may form a user profile associated with the user.The user profile may be updated, modified, and/or otherwise alteredbased on analysis of information aggregated from the one or moreinformation channels 114 (e.g., applications 160, systems 170, systeminformation data stores 180). In some cases, the machine learning module134, in combination with the knowledge base data store(s) 136, mayapproximate the mood associated with the user in the user profile. Theknowledge base data store(s) 136 may contain information associatingspecific information with specific emotions (e.g., happiness, sadness,anger, and the like) in order to generate a mood for the user profile,which may influence decision-making processes associated with theintelligent agent system 120.

In some cases, the machine learning module 134 may generate baselinemood information in the knowledge base data store(s) 136 and determinespecific emotions based on analysis of the aggregated information asdeviation from the baseline mood information of the user profile. Forexample, if the machine learning module 134 receives information from asocial media application (e.g., application 162) indicating it is theuser's birthday, the machine learning module 134 may associate happinesswith the user's birthday and add the corresponding information to theuser profile for decision-making processes. Additionally, for example,the machine learning module 134 may associate anger with receivedinformation from a messaging application (e.g., application 162) incombination with a user's input (e.g., typing speed, typing pressure,gesture acceleration information) at a touch sensitive display device ofthe computing device 102. Based on the mood information of the userprofile in the knowledge base data store(s) 136, the intelligent agentsystem 120 may initiate execution of one or more targeted actionsassociated with specific mood information.

In some cases, the intelligent agent system 120 may include one or moreperformance information data stores 138. The one or more performanceinformation data stores 138 may be accessible and/or provide informationto the aggregation module 132 and/or a decision processing module 140.The performance information data store(s) 138 may include historicalinformation associated with the decisioning system 130 and/orintelligent agent system 120. In some cases, the performance informationdata store(s) 138 may include historical information for each decisionmade by a decision processing module 140 of the decisioning system 130.The historical information may include information associated withprevious decision-making processes of the intelligent agent system 120including the decision made, the action executed as a result of thedecision, user intervention associated with the decision (e.g., a userallowed and/or denied the decision at the user interface module 124),the determined confidence level for the decision, and/or user feedbackassociated with the decision. The confidence level for a decision may bea numerical score and/or percentage calculated based on the confidenceof the intelligent agent system 120 in executing a specific decision. Insome cases, the confidence level associated with a decision may changein response to received user feedback at the user interface module 124and be used by the machine learning module to improve operation of theintelligent agent system 120.

As an example, the intelligent agent system 120 make execute a decisionto generate a restaurant recommendation at the user interface module 124based on aggregated information. The historical information stored inthe performance information data store(s) 138 may include the decisionto generate the recommendation of a particular restaurant to the user,received feedback at the user interface module 124 that the useraccepted the recommendation, location information that the user traveledto the recommended restaurant accessed from the mobile device locationsensing device, received feedback at the user interface module 124 thatthe user enjoyed their experience (e.g., a user interface screenprovided to the user when the intelligent agent system 120 sensed thatthe user left the location of the recommended restaurant, and an initialcalculated confidence level of 78 percent, may result in a re-calculatedconfidence level of 84 percent after receiving feedback.

In some cases the intelligent agent system 120 may include a decisionprocessing module 140. The decision processing module 140 may access,receive, and/or store information data stores associated with themachine learning module 134, one or more knowledge base data stores 136,and/or one or more performance information data stores 138. The decisionprocessing module 140 may aggregate the information from one or more ofthe machine learning module 134, knowledge base data store 136, andperformance information data store 138 to analyze the aggregatedinformation, determine if the aggregated information may result in anexecutable action, initiate execution of the executable action, anddocument the result of an executed action. The decision processingmodule 140 may directly execute and/or trigger one or more applications160 and/or systems 170 of the computing device 102 to execute one ormore actions associated with the one or more applications 160, systems170, and/or one or more system information data stores 180. In somecases, the decision processing module 140 may trigger the computingdevice 102 to execute one or more actions at the one or moreapplications 160 and/or systems 170 based on a determined confidencelevel for a potential decision.

In some cases, based on the aggregated information from the machinelearning module 134, the knowledge base data store(s) 136, and theperformance information data store(s) 138, the decision processingmodule 140 may calculate a confidence level for the potential decision.The confidence level may be numeric and/or be based on a weightedcombination of the information available to the decision processingmodule 140 from the machine learning module 134, knowledge base datastore(s) 136, and performance information data store(s) 138. Forexample, in determining a confidence level in a potential decision, thedecision processing module 140 may more heavily weigh information fromthe knowledge base data store(s) 136, and less heavily weigh informationfrom the performance information data store(s) 138. The decisionprocessing module 140 may compare the confidence level for a potentialdecision to a decisioning threshold. The decisioning threshold may be anumerical value (e.g., a percentage, a numerical value between 0-1, andthe like). In some cases, the decisioning threshold may be a fixed valueand/or floating value of the decision processing module 140. In somecases, the decisioning threshold may be assigned at the user interfacemodule 124 by a user of the computing device 102. For example, a usermay configure the intelligent agent system 120 to execute decisions witha high confidence value (e.g., a 90 percent confidence value, a 0.9confidence factor) by directly configuring the decision threshold to avalue corresponding to a high confidence value at the user interfacemodule 124.

In some cases, after a decision to execute an action (e.g., trigger oneor more applications 160 and/or systems 170 perform an action) is madeby the decision processing module 140, the decision processing module140 may store information associated with the decision in the knowledgebase data store(s) 136 and/or the performance information data store(s)138 as described herein. For example, the decision processing module 140may store the confidence level associated with the decision in theperformance information data store(s) 138. In some cases, the executedaction may include notification to the user. For example, an executeddecision to recommend traffic information to a user may result in agenerated notification to be displayed to the user. A notification maybe generated and/or triggered to be generated (e.g., at a notificationsystem 172 of the computing device 102) by the decision processingmodule 140. For example, for an executed decision that results inoffering an automobile loan to a user, the decision processing module140 may communicate to a financial application (e.g., application 162)to generate and display a notification at the user interface of thefinancial application that includes an automobile loan offer for theuser. In some cases, the notification may be displayed at the userinterface module 124 of the intelligent agent system 120, displayed at auser interface of a notification system (e.g., system 172) native to anoperating system of the computing device 102, communicated as a message(e.g., an email message, a text message, and the like) to an application162 and/or system 172 of the computing device 102, and/or otherwise madeavailable at the computing device 102 by another method.

FIG. 2A shows an illustrative event sequence 200 for decision processingof aggregated information in the intelligent agent system 120 inaccordance with one or more aspects described herein. The events shownin the illustrative event sequence 200 are illustrative and additionalevents may be added, or events, may be omitted, without departing fromthe scope of the disclosure. At 202, the aggregation module 132 mayaggregate information for the information channel(s) (e.g., applications160, systems 170, system information data stores 180) of the computingdevice 102. The aggregation module 132 may fetch and/or receiveinformation from the information channel(s) at 202 as described herein.At 204, the machine learning module 134 may analyze the aggregatedinformation from the aggregation module 132. The machine learning module134 may analyze the aggregated information to determine whether theinformation may result in an executable action associated with thecomputing device 102. At 206, the machine learning module 134 maycompare the analyzed information to the one or more knowledge base datastores 136 to determine if the analyzed information is relevant anexecutable action at the information channels 114 of the computingdevice 102 (e.g., applications 160, systems 170, system information datastores 180). For example, aggregated information from a social mediapost by the user may indicate that the user is interested in a musician.Accordingly, that information may be determined by the machine learningmodule 134 to be relevant in the context of the contents of theknowledge base data store(s) 136, as it may be used by the intelligentagent system 120 to recommend, via a user interface module 124 of theintelligent agent system 120, message via a notification system (e.g.,system 172) of the computing device 102, or other method, concerttickets associated with the musician to the user.

At 210, based on the comparison of the analyzed information to theknowledge base data store(s) 136 by the machine learning module 134, themachine learning module 134 may determine if the analyzed information isrelevant. If the analyzed information is determined to not be relevantat 210, the illustrative event sequence 200 for decision processing mayend. If the analyzed information is determined to be relevantinformation at 210, the machine learning module 134 and/or the decisionprocessing module 140 may determine the decision that corresponds to therelevant information at 212. For example, based on user preferences inthe knowledge base data store(s) 136, the machine learning module 134may determine that internet browsing data for automobile loans may beused to recommend personalized automobile loans based on a user's creditscore in a financial application (e.g., 160) associated with theintelligent agent system 120. In some cases, more than one possibledecision associated with the relevant information may exist at 212.After determining the decision associated with the relevant informationat 212, the machine learning module 134 may store the relevantinformation in the knowledge base data store(s) 136 at 214 to update theuser profile associated with the user.

After updating the knowledge base data store(s) 136 at 214, the machinelearning module 134 and/or the decision processing module 140 maydetermine if the relevant information may correspond to an executableaction for one or more of the elements (e.g., applications 160, systems170, system information data stores 180, hardware components) of thecomputing device 102 at 215. If the relevant information is determinedto correspond an executable action at 215, the illustrative eventsequence 200 for decision processing may end. For example, analysis of atext message from a messaging application (e.g., 160) by the machinelearning module 134 may allow the machine learning module to determine amood (e.g., happy, sad, angry, and the like) of the user. Thedetermination may be added to the user profile in the knowledge basedata store(s) 136 to influence future decision-making processes, but maynot directly result in a corresponding executable action. If therelevant information is determined to correspond to an executable actionat 215, the decision processing module 140 may compare the executableaction to historical decisioning information (e.g., previous associateddecisions, confidence factors, and the like) stored in the performanceinformation data store(s) 138 at 218.

FIG. 2B shows an illustrative event sequence 200 for decision processingof aggregated information in the intelligent agent system 120 inaccordance with one or more aspects described herein. The events shownin the illustrative event sequence 200 are illustrative and additionalevents may be added, or events, may be omitted, without departing fromthe scope of the disclosure. Continuing from FIG. 2B, based on thecomparison of the executable action to historical decisioninginformation at 218 and/or the information of the knowledge base datastore(s) 136, the decision processing module 140 may determine theconfidence factor for the decision as described herein at 222. At 225,the determined confidence factor may be compared to the definedconfidence threshold associated with initiating execution of the action.If the determined confidence factor fails to exceed the confidencethreshold at 225, the illustrative event sequence 200 for decisionprocessing may end. If the determined confidence factor exceeds theconfidence threshold at 225, the decision processing module 140 mayaccess the knowledge base data store(s) 136 to determine the grantedand/or denied permissions associated with the decision at 228. Forexample, the knowledge base data store(s) 136 may contain informationgranting the intelligent agent system 120 the ability to display stockmarket information at the user interface module 124 based on informationaggregated from a financial market application (e.g., 160). If thedecision processing module 140 determines there exists permission toinitiate execution of the action in the knowledge base data store(s) 136at 235, the decision processing module 140 may initiate execution of theaction at 238. After initiating execution of the action at 238, thedecision processing module 140 may store information associated with theexecuted action and information associated with the decision to initiateexecution of the action in the knowledge base data store(s) 136 and/orthe performance information data store(s) 138 at 242.

If the decision processing module 140 determines permission does notexist in the knowledge base data store(s) 136 to initiate execution ofthe action, the decision processing module 140 may determine if theintelligent agent system 120 is configured to request permission at theuser interface module 124 to initiate execution of the action at 245. Insome cases, the intelligent agent system 120 may be configured torequest permission to initiate execution of specific actions. Forexample, at the user interface module 124, a user may configure theintelligent agent system 120 to request permission for an executableaction for which the user has not explicitly denied permission.Additionally or alternatively, the intelligent agent system 120 may beconfigured not to request permission to initiate execution of specificactions. If the intelligent agent system 120 and/or the knowledge basedata stores 136 contain information denying the ability to request userpermission at 245, the illustrative event sequence 200 for decisionprocessing may end. If the intelligent agent system 120 and/or theknowledge base data stores 136 contain information granting the abilityrequest user permission at 245, the decision processing module 140 maygenerate a notification for display at the user interface module 124and/or at the operating system of the computing device 102 at 248, wherethe notification includes a request for user permission to initiateexecution of the action.

At 250, the decision processing module 140 may determine if a responseto the request for user permission has been received at the userinterface module 124. If a response to the request for user permissionhas not been received at 250, the illustrative event sequence 200 fordecision processing may end. The decision processing module 140 mayallow a duration of time (e.g., 30 seconds, 1 minute, 5 minutes, and thelike) for a user to input a response to the request for user permissionat 250. After the duration of time expires, absent a received responseat the user interface module 124, the decision processing module 140 mayinterpret the lack of a response as denying permission to initiateexecution of the action. If a response to the request for userpermission is received at 250, the decision processing module 140 maydetermine if the received permission response contains informationgranting the intelligent agent system 120 the ability to initiateexecution of the action at 255. If the received permission responsecontains information denying the decision processing module 140 theability to initiate execution of the action, the decision processingmodule 140 may store information associated with the denied action inthe knowledge base data store(s) 136 and/or the performance informationdata store(s) 138 at 242. If the received permission response containsinformation granting the decision processing module 140 the ability toinitiate execution of the action, the decision processing module 140 mayinitiate execution of the action at 238. After initiating execution ofthe action at 238, the decision processing module 140 may storeinformation associated with the executed action as described herein at242 and the illustrative event sequence 200 for decision processing mayend. It is to be noted that multiple instances of the illustrative eventsequence 200 may occur concurrently and/or successively and that theillustrative event sequence 200 may be a continuous process based on theflow of information from the one or more information channels 114 to theaggregation module 132.

FIG. 3 shows an illustrative operating environment in which variousaspects of the present disclosure may be implemented in accordance withone or more example embodiments. Referring to FIG. 3, a computing systemenvironment 300 may be used according to one or more illustrativeembodiments. The computing system environment 300 is only one example ofa suitable computing environment and is not intended to suggest anylimitation as to the scope of use or functionality contained in thedisclosure. The computing system environment 300 should not beinterpreted as having any dependency or requirement relating to any oneor combination of components shown in the illustrative computing systemenvironment 300.

The computing system environment 300 may include an illustrativeintelligent agent computing device 301 having a processor 303 forcontrolling overall operation of the intelligent agent computing device301 and its associated components, including a Random Access Memory(RAM) 305, a Read-Only Memory (ROM) 307, a communications module 309,and a memory 315. The intelligent agent computing device 301 may includea variety of computer readable media. Computer readable media may be anyavailable media that may be accessed by the intelligent agent computingdevice 301, may be non-transitory, and may include volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, object code, data structures, program modules, or otherdata. Examples of computer readable media may include Random AccessMemory (RAM), Read Only Memory (ROM), Electronically ErasableProgrammable Read-Only Memory (EEPROM), flash memory or other memorytechnology, Compact Disk Read-Only Memory (CD-ROM), Digital VersatileDisk (DVD) or other optical disk storage, magnetic cassettes, magnetictape, magnetic disk storage or other magnetic storage devices, or anyother medium that can be used to store the desired information and thatcan be accessed by the intelligent agent computing device 301.

Although not required, various aspects described herein may be embodiedas a method, a data transfer system, or as a computer-readable mediumstoring computer-executable instructions. For example, acomputer-readable medium storing instructions to cause a processor toperform steps of a method in accordance with aspects of the disclosedembodiments is contemplated. For example, aspects of method stepsdisclosed herein may be executed by the processor 303 of the intelligentagent computing device 301. Such a processor may executecomputer-executable instructions stored on a computer-readable medium.

Software may be stored within the memory 315 and/or other digitalstorage to provide instructions to the processor 303 for enabling theintelligent agent computing device 301 to perform various functions asdiscussed herein. For example, the memory 315 may store software used bythe intelligent agent computing device 301, such as an operating system317, one or more application programs 319, and/or an associated database321. In addition, some or all of the computer executable instructionsfor the intelligent agent computing device 301 may be embodied inhardware or firmware. Although not shown, the RAM 305 may include one ormore applications representing the application data stored in the RAM305 while the intelligent agent computing device 301 is on andcorresponding software applications (e.g., software tasks) are runningon the intelligent agent computing device 301.

The communications module 309 may include a microphone, a keypad, atouch screen, and/or a stylus through which a user of the intelligentagent computing device 301 may provide input, and may include one ormore of a speaker for providing audio output and a video display devicefor providing textual, audiovisual and/or graphical output. Thecomputing system environment 300 may also include optical scanners (notshown).

The intelligent agent computing device 301 may operate in a networkedenvironment supporting connections to one or more remote computingdevices, such as the computing devices 341 and 351. The computingdevices 341 and 351 may be personal computing devices or servers thatinclude any or all of the elements described above relative to theintelligent agent computing device 301.

The network connections depicted in FIG. 3 may include a Local AreaNetwork (LAN) 325 and/or a Wide Area Network (WAN) 329, as well as othernetworks. When used in a LAN networking environment, the intelligentagent computing device 301 may be connected to the LAN 325 through anetwork interface or adapter in the communications module 309. When usedin a WAN networking environment, the intelligent agent computing device301 may include a modem in the communications module 309 or other meansfor establishing communications over the WAN 329, such as a network 331(e.g., public network, private network, Internet, intranet, and thelike). The network connections shown are illustrative and other means ofestablishing a communications link between the computing devices may beused. Various well-known protocols such as Transmission ControlProtocol/Internet Protocol (TCP/IP), Ethernet, File Transfer Protocol(FTP), Hypertext Transfer Protocol (HTTP) and the like may be used, andthe system can be operated in a client-server configuration to permit auser to retrieve web pages from a web-based server. Any of variousconventional web browsers can be used to display and manipulate data onweb pages.

The disclosure is operational with numerous other computing systemenvironments or configurations. Examples of computing systems,environments, and/or configurations that may be suitable for use withthe disclosed embodiments include, but are not limited to, personalcomputers (PCs), server computers, hand-held or laptop devices, smartphones, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputers,mainframe computers, distributed computing environments that include anyof the above systems or devices, and the like that are configured toperform the functions described herein.

FIG. 4 shows an illustrative block diagram of workstations and serversthat may be used to implement the processes and functions of certainaspects of the present disclosure in accordance with one or more exampleembodiments. For example, an illustrative system 400 may be used forimplementing illustrative embodiments according to the presentdisclosure. As illustrated, the system 400 may include one or moreworkstation computers 401. The workstation 401 may be, for example, adesktop computer, a smartphone, a wireless device, a tablet computer, alaptop computer, and the like, configured to perform various processesdescribed herein. The workstations 401 may be local or remote, and maybe connected by one of the communications links 402 to a computernetwork 403 that is linked via the communications link 405 to theintelligent agent server 404. In the system 400, the intelligent agentserver 404 may be a server, processor, computer, or data processingdevice, or combination of the same, configured to perform the functionsand/or processes described herein. The intelligent agent server 404 maybe used to receive check images and associated data and/or validationscores, retrieve user profile, evaluate the check image compared to theuser profile, identify matching or non-matching elements, generate userinterfaces, and the like.

The computer network 403 may be any suitable computer network includingthe Internet, an intranet, a Wide-Area Network (WAN), a Local-AreaNetwork (LAN), a wireless network, a Digital Subscriber Line (DSL)network, a frame relay network, an Asynchronous Transfer Mode network, aVirtual Private Network (VPN), or any combination of any of the same.The communications links 402 and 405 may be communications linkssuitable for communicating between the workstations 401 and theintelligent agent server 404, such as network links, dial-up links,wireless links, hard-wired links, as well as network types developed inthe future, and the like.

One or more aspects of the disclosure may be embodied in computer-usabledata or computer-executable instructions, such as in one or more programmodules, executed by one or more computers or other devices to performthe operations described herein. Generally, program modules includeroutines, programs, objects, components, data structures, and the likethat perform particular tasks or implement particular abstract datatypes when executed by one or more processors in a computer or otherdata processing device. The computer-executable instructions may bestored as computer-readable instructions on a computer-readable mediumsuch as a hard disk, optical disk, removable storage media, solid-statememory, RAM, and the like. The functionality of the program modules maybe combined or distributed as desired in various embodiments. Inaddition, the functionality may be embodied in whole or in part infirmware or hardware equivalents, such as integrated circuits,Application-Specific Integrated Circuits (ASICs), Field ProgrammableGate Arrays (FPGA), and the like. Particular data structures may be usedto more effectively implement one or more aspects of the disclosure, andsuch data structures are contemplated to be within the scope of computerexecutable instructions and computer-usable data described herein.

Various aspects described herein may be embodied as a method, anapparatus, or as one or more computer-readable media storingcomputer-executable instructions. Accordingly, those aspects may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, an entirely firmware embodiment, or an embodiment combiningsoftware, hardware, and firmware aspects in any combination. Inaddition, various signals representing data or events as describedherein may be transferred between a source and a destination in the formof light or electromagnetic waves traveling through signal-conductingmedia such as metal wires, optical fibers, or wireless transmissionmedia (e.g., air or space). In general, the one or morecomputer-readable media may be and/or include one or more non-transitorycomputer-readable media.

As described herein, the various methods and acts may be operativeacross one or more computing servers and one or more networks. Thefunctionality may be distributed in any manner, or may be located in asingle computing device (e.g., a server, a client computer, and thelike). For example, in alternative embodiments, one or more of thecomputing platforms discussed above may be combined into a singlecomputing platform, and the various functions of each computing platformmay be performed by the single computing platform. In such arrangements,any and/or all of the above-discussed communications between computingplatforms may correspond to data being accessed, moved, modified,updated, and/or otherwise used by the single computing platform.Additionally or alternatively, one or more of the computing platformsdiscussed above may be implemented in one or more virtual machines thatare provided by one or more physical computing devices. In sucharrangements, the various functions of each computing platform may beperformed by the one or more virtual machines, and any and/or all of theabove-discussed communications between computing platforms maycorrespond to data being accessed, moved, modified, updated, and/orotherwise used by the one or more virtual machines.

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one or more of the steps depicted in theillustrative figures may be performed in other than the recited order,one or more steps described with respect to one figure may be used incombination with one or more steps described with respect to anotherfigure, and/or one or more depicted steps may be optional in accordancewith aspects of the disclosure.

1. A system, comprising: a computing device comprising a data storestoring historical decisioning information; a computing platformcomprising: at least one processor; a communication interfacecommunicatively coupled via a network to the computing device; andmemory storing computer-readable instructions that, when executed by theat least one processor, cause the computing platform to: identify,automatically by a machine learning module and based on aggregatedinformation received from a plurality of information channels, relevantinformation for automatic decisioning; identify, by the machine learningmodule and based on a knowledge base and the relevant information, anexecutable action to be performed by an application associated with thecomputing platform; generate, based on the historical decisioninginformation, a confidence level associated with initiating theexecutable action by the application, wherein the confidence levelcorresponds to a user satisfaction with performance of the executableaction by the application; cause execution, by a decision processingmodule and based on the confidence level, a decision threshold, and userpermission information, of the executable action at a system of thecomputing platform associated with the executable action; and update,via the network, the historical decisioning information with decisioninginformation associated with the executable action initiated at thesystem of the computing platform, wherein the decisioning informationcomprises a description of the executable action, the confidence level,the decision threshold, and user feedback associated with the executableaction.
 2. The system of claim 1, wherein the instructions further causethe computing platform to: compare, by the decision processing module,the confidence level to the decision threshold associated with theexecutable action, wherein the decision threshold is determined based onanalysis of the knowledge base, the historical decisioning information,and the user feedback.
 3. The system of claim 1, wherein theinstructions further cause the computing platform to: identify, based onthe executable action and at the knowledge base, the user permissioninformation associated with the executable action.
 4. The system ofclaim 1, wherein the instructions further cause the computing platformto: cause display, by the decision processing module and at a userinterface of the computing platform, a request for user permissioninformation associated with the executable action.
 5. The system ofclaim 4, wherein the instructions further cause the computing platformto: receive, by a user interface of the computing platform, a permissionresponse, wherein the permission response comprises informationindicating approval or disapproval of the executable action; and causestorage, at the knowledge base, of the permission response.
 6. Thesystem of claim 1, wherein the instructions further cause the computingplatform to: configure accessibility settings for the aggregation moduleto deny or allow the aggregation module to access an information channelof the plurality of information channels.
 7. The system of claim 1,wherein the instructions further cause the computing platform to:receive, by a user interface of the computing platform, the userfeedback comprising information indicating the user satisfaction levelwith the executable action; and cause storage, at the knowledge base andwith the historical decisioning information, the user feedback.
 8. Thesystem of claim 1, wherein the plurality of information channelscomprises account information, applications, systems associated with thecomputing platform, and data gathered from peripherals of the computingplatform.
 9. A method, comprising: at a computing platform comprisingone or more processors, memory, and a communication interface:identifying, automatically by a machine learning module and based onaggregated information received from a plurality of information channelsand based on a knowledge base, relevant information for automaticdecisioning; identifying, by the machine learning module and based onthe knowledge base and the relevant information, an executable action tobe performed by an application associated with the computing platform;generating, based on the knowledge base and historical decisioninginformation, a confidence level associated with initiating theexecutable action by the application, wherein the confidence levelcorresponds to a user satisfaction with performance of the executableaction by the application; causing execution, by a decision processingmodule and based on the confidence level, a decision threshold, and userpermission information, of the executable action at a system of thecomputing platform; and updating the historical decisioning informationwith decisioning information associated with the executable action,wherein the decisioning information comprises a description of theexecutable action, the confidence level, the decision threshold, anduser feedback associated with the executable action.
 10. The method ofclaim 9, comprising: determining, based on analysis of the knowledgebase, the historical decisioning information, and the user feedback, thedecision threshold; and comparing, by the decision processing module,the confidence level to the decision threshold.
 11. The method of claim9, comprising: identifying, based on the executable action and theknowledge base, the user permission information associated with theexecutable action.
 12. The method of claim 9, comprising: generating, bythe decision processing module, a notification comprising a request foruser permission information associated with the executable action. 13.The method of claim 9, comprising: receiving, by a user interface of thecomputing platform, the user feedback comprising information indicatingthe user satisfaction level with the executable action; and storing, atthe knowledge base and with the historical decisioning information, theuser feedback.
 14. The method of claim 9, comprising: configuringaccessibility settings to an information channel of the plurality ofinformation channels for the aggregation module.
 15. One or morenon-transitory computer-readable media storing instructions that, whenexecuted by a computing platform comprising at least one processor,memory, and a communication interface, cause the computing platform to:identify, automatically by a machine learning module and based onaggregated information received from a plurality of informationchannels, relevant information for automatic decisioning; identify, bythe machine learning module and based on a knowledge base and therelevant information, an executable action to be performed by anapplication associated with the computing platform; generate, based onhistorical decisioning information, a confidence level associated withinitiating the executable action by the application, wherein theconfidence level corresponds to a user satisfaction with performance ofthe executable action by the application; cause execution, by a decisionprocessing module and based on the confidence level, a decisionthreshold, and user permission information, of the executable action ata system of the computing platform associated with the executableaction; and update, via a network, the historical decisioninginformation with decisioning information associated with the executableaction initiated at the system of the computing platform, wherein thedecisioning information comprises a description of the executableaction, the confidence level, the decision threshold, and user feedbackassociated with the executable action.
 16. The one or morenon-transitory computer-readable media of claim 15, wherein theinstructions, when executed by the one or more processors, cause thecomputing platform to: determine, based on analysis of the knowledgebase, the historical decisioning information, and the user feedback, thedecision threshold; and compare, by the decision processing module, theconfidence level to the decision threshold.
 17. The one or morenon-transitory computer-readable media of claim 15, wherein theinstructions, when executed by the one or more processors, cause thecomputing platform to: identify, based on the executable action and theknowledge base, the user permission information.
 18. The one or morenon-transitory computer-readable media of claim 15, wherein theinstructions, when executed by the one or more processors, cause thecomputing platform to: generate a notification comprising a request foruser permission information associated with the executable action;receive, at a user interface of the computing platform, a permissionresponse comprising information indicating approval or disapproval ofthe executable action; and store, at the knowledge base, the permissionresponse.
 19. The one or more non-transitory computer-readable media ofclaim 15, wherein the instructions, when executed by the one or moreprocessors, cause the computing platform to: configure accessibilitysettings to an information channel of the plurality of informationchannels for the aggregation module.
 20. The one or more non-transitorycomputer-readable media of claim 15, wherein the instructions, whenexecuted by the one or more processors, cause the computing platform to:receive, by a user interface of the computing platform, the userfeedback associated with the executable action, wherein the userfeedback comprises information indicating a user satisfaction level withthe executable action; and store, at the knowledge base and with thehistorical decisioning information, the user feedback associated withthe executable action.